As typography is a core design element of any printed or displayed text, graphic designers are frequently interested in typefaces. With the advent of computers and digital typography, the number of different typefaces has continued to grow. Typefaces may include one or more fonts that share common design features. Each font of a typeface includes attributes specific to that font (e.g., weight, slope, width, optimal size, serif, etc.). Given this large number of available fonts, the task of identifying fonts with desired attributes in a collection of typefaces has become an extremely tedious and time-consuming process.
In particular, there are a number of scenarios in which it may be desirable to identify similar fonts among a collection of fonts. For instance, a graphic designer may identify a font that is cost-prohibitive and desires to find a free alternative. In another instance, a graphic designer may desire to identify a particular font but a font recognition system is unable to precisely identify the font. Current systems are limited to providing a list based recommendation of fonts and do not provide a visualization of similarities that may be browsed and navigated allowing the user to select the desired font.